Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations45379
Missing cells85250
Missing cells (%)9.4%
Duplicate rows27
Duplicate rows (%)0.1%
Total size in memory54.1 MiB
Average record size in memory1.2 KiB

Variable types

Text10
Numeric7
Unsupported1
DateTime1
Categorical1

Alerts

Dataset has 27 (0.1%) duplicate rowsDuplicates
budget is highly overall correlated with return and 1 other fieldsHigh correlation
return is highly overall correlated with budget and 1 other fieldsHigh correlation
revenue is highly overall correlated with budget and 2 other fieldsHigh correlation
vote_count is highly overall correlated with revenueHigh correlation
status is highly imbalanced (97.0%) Imbalance
belongs_to_collection has 40891 (90.1%) missing values Missing
genres has 2384 (5.3%) missing values Missing
overview has 941 (2.1%) missing values Missing
production_companies has 11799 (26.0%) missing values Missing
spoken_languages has 3894 (8.6%) missing values Missing
tagline has 24981 (55.0%) missing values Missing
return is highly skewed (γ1 = 138.3340992) Skewed
popularity is an unsupported type, check if it needs cleaning or further analysis Unsupported
budget has 36493 (80.4%) zeros Zeros
revenue has 37972 (83.7%) zeros Zeros
runtime has 1535 (3.4%) zeros Zeros
vote_average has 2947 (6.5%) zeros Zeros
vote_count has 2849 (6.3%) zeros Zeros
return has 39998 (88.1%) zeros Zeros

Reproduction

Analysis started2024-10-30 23:39:42.623918
Analysis finished2024-10-30 23:40:14.831903
Duration32.21 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

belongs_to_collection
Text

Missing 

Distinct1695
Distinct (%)37.8%
Missing40891
Missing (%)90.1%
Memory size1.6 MiB
2024-10-30T20:40:15.527905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length54
Median length43
Mean length23.855838
Min length3

Characters and Unicode

Total characters107065
Distinct characters166
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)8.7%

Sample

1st rowToy Story Collection
2nd rowGrumpy Old Men Collection
3rd rowFather of the Bride Collection
4th rowJames Bond Collection
5th rowBalto Collection
ValueCountFrequency (%)
collection 3743
25.3%
the 1146
 
7.8%
of 230
 
1.6%
series 147
 
1.0%
139
 
0.9%
trilogy 87
 
0.6%
and 84
 
0.6%
man 62
 
0.4%
a 62
 
0.4%
in 56
 
0.4%
Other values (2407) 9028
61.1%
2024-10-30T20:40:16.583900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 11114
 
10.4%
e 10450
 
9.8%
10297
 
9.6%
l 10200
 
9.5%
i 7559
 
7.1%
n 7403
 
6.9%
t 6488
 
6.1%
c 4845
 
4.5%
C 4474
 
4.2%
a 4459
 
4.2%
Other values (156) 29776
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107065
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 11114
 
10.4%
e 10450
 
9.8%
10297
 
9.6%
l 10200
 
9.5%
i 7559
 
7.1%
n 7403
 
6.9%
t 6488
 
6.1%
c 4845
 
4.5%
C 4474
 
4.2%
a 4459
 
4.2%
Other values (156) 29776
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107065
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 11114
 
10.4%
e 10450
 
9.8%
10297
 
9.6%
l 10200
 
9.5%
i 7559
 
7.1%
n 7403
 
6.9%
t 6488
 
6.1%
c 4845
 
4.5%
C 4474
 
4.2%
a 4459
 
4.2%
Other values (156) 29776
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107065
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 11114
 
10.4%
e 10450
 
9.8%
10297
 
9.6%
l 10200
 
9.5%
i 7559
 
7.1%
n 7403
 
6.9%
t 6488
 
6.1%
c 4845
 
4.5%
C 4474
 
4.2%
a 4459
 
4.2%
Other values (156) 29776
27.8%

budget
Real number (ℝ)

High correlation  Zeros 

Distinct1223
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4232324.6
Minimum0
Maximum3.8 × 108
Zeros36493
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size354.7 KiB
2024-10-30T20:40:16.879901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25000000
Maximum3.8 × 108
Range3.8 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17439317
Coefficient of variation (CV)4.1205056
Kurtosis66.63901
Mean4232324.6
Median Absolute Deviation (MAD)0
Skewness7.1185794
Sum1.9205866 × 1011
Variance3.0412978 × 1014
MonotonicityNot monotonic
2024-10-30T20:40:17.207944image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36493
80.4%
5000000 286
 
0.6%
10000000 259
 
0.6%
20000000 243
 
0.5%
2000000 242
 
0.5%
15000000 226
 
0.5%
3000000 223
 
0.5%
25000000 206
 
0.5%
1000000 197
 
0.4%
30000000 190
 
0.4%
Other values (1213) 6814
 
15.0%
ValueCountFrequency (%)
0 36493
80.4%
1 25
 
0.1%
2 14
 
< 0.1%
3 9
 
< 0.1%
4 8
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
380000000 1
 
< 0.1%
300000000 1
 
< 0.1%
280000000 1
 
< 0.1%
270000000 1
 
< 0.1%
260000000 3
 
< 0.1%
258000000 1
 
< 0.1%
255000000 1
 
< 0.1%
250000000 10
< 0.1%
245000000 2
 
< 0.1%
237000000 1
 
< 0.1%

genres
Text

Missing 

Distinct4067
Distinct (%)9.5%
Missing2384
Missing (%)5.3%
Memory size2.8 MiB
2024-10-30T20:40:17.543906image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length84
Median length70
Mean length16.467054
Min length3

Characters and Unicode

Total characters708001
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2367 ?
Unique (%)5.5%

Sample

1st rowAnimation, Comedy, Family
2nd rowAdventure, Fantasy, Family
3rd rowRomance, Comedy
4th rowComedy, Drama, Romance
5th rowComedy
ValueCountFrequency (%)
drama 20255
21.4%
comedy 13181
13.9%
thriller 7619
 
8.0%
romance 6733
 
7.1%
action 6592
 
6.9%
horror 4670
 
4.9%
crime 4305
 
4.5%
documentary 3921
 
4.1%
adventure 3494
 
3.7%
science 3042
 
3.2%
Other values (36) 21059
22.2%
2024-10-30T20:40:18.231902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 69082
 
9.8%
a 61822
 
8.7%
e 55786
 
7.9%
m 53101
 
7.5%
51876
 
7.3%
o 48541
 
6.9%
, 48053
 
6.8%
i 39670
 
5.6%
n 35676
 
5.0%
y 28510
 
4.0%
Other values (30) 215884
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 708001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 69082
 
9.8%
a 61822
 
8.7%
e 55786
 
7.9%
m 53101
 
7.5%
51876
 
7.3%
o 48541
 
6.9%
, 48053
 
6.8%
i 39670
 
5.6%
n 35676
 
5.0%
y 28510
 
4.0%
Other values (30) 215884
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 708001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 69082
 
9.8%
a 61822
 
8.7%
e 55786
 
7.9%
m 53101
 
7.5%
51876
 
7.3%
o 48541
 
6.9%
, 48053
 
6.8%
i 39670
 
5.6%
n 35676
 
5.0%
y 28510
 
4.0%
Other values (30) 215884
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 708001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 69082
 
9.8%
a 61822
 
8.7%
e 55786
 
7.9%
m 53101
 
7.5%
51876
 
7.3%
o 48541
 
6.9%
, 48053
 
6.8%
i 39670
 
5.6%
n 35676
 
5.0%
y 28510
 
4.0%
Other values (30) 215884
30.5%

id
Text

Distinct45349
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-10-30T20:40:19.007944image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.250292
Min length1

Characters and Unicode

Total characters238253
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45320 ?
Unique (%)99.9%

Sample

1st row862
2nd row8844
3rd row15602
4th row31357
5th row11862
ValueCountFrequency (%)
141971 3
 
< 0.1%
14788 2
 
< 0.1%
23305 2
 
< 0.1%
11115 2
 
< 0.1%
77221 2
 
< 0.1%
69234 2
 
< 0.1%
4912 2
 
< 0.1%
110428 2
 
< 0.1%
265189 2
 
< 0.1%
13209 2
 
< 0.1%
Other values (45339) 45358
> 99.9%
2024-10-30T20:40:20.023903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 32853
13.8%
2 28565
12.0%
3 26667
11.2%
4 24684
10.4%
5 21956
9.2%
6 21129
8.9%
7 20905
8.8%
8 20875
8.8%
9 20447
8.6%
0 20166
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 32853
13.8%
2 28565
12.0%
3 26667
11.2%
4 24684
10.4%
5 21956
9.2%
6 21129
8.9%
7 20905
8.8%
8 20875
8.8%
9 20447
8.6%
0 20166
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 32853
13.8%
2 28565
12.0%
3 26667
11.2%
4 24684
10.4%
5 21956
9.2%
6 21129
8.9%
7 20905
8.8%
8 20875
8.8%
9 20447
8.6%
0 20166
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 32853
13.8%
2 28565
12.0%
3 26667
11.2%
4 24684
10.4%
5 21956
9.2%
6 21129
8.9%
7 20905
8.8%
8 20875
8.8%
9 20447
8.6%
0 20166
8.5%
Distinct92
Distinct (%)0.2%
Missing11
Missing (%)< 0.1%
Memory size2.2 MiB
2024-10-30T20:40:20.343947image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.0001543
Min length2

Characters and Unicode

Total characters90743
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 32202
71.0%
fr 2437
 
5.4%
it 1528
 
3.4%
ja 1349
 
3.0%
de 1078
 
2.4%
es 992
 
2.2%
ru 822
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 408
 
0.9%
Other values (82) 3600
 
7.9%
2024-10-30T20:40:20.887945image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34527
38.0%
n 32910
36.3%
r 3630
 
4.0%
f 2835
 
3.1%
i 2388
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1323
 
1.5%
Other values (23) 6039
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 34527
38.0%
n 32910
36.3%
r 3630
 
4.0%
f 2835
 
3.1%
i 2388
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1323
 
1.5%
Other values (23) 6039
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 34527
38.0%
n 32910
36.3%
r 3630
 
4.0%
f 2835
 
3.1%
i 2388
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1323
 
1.5%
Other values (23) 6039
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 34527
38.0%
n 32910
36.3%
r 3630
 
4.0%
f 2835
 
3.1%
i 2388
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1323
 
1.5%
Other values (23) 6039
 
6.7%

overview
Text

Missing 

Distinct44233
Distinct (%)99.5%
Missing941
Missing (%)2.1%
Memory size19.6 MiB
2024-10-30T20:40:21.639902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length1000
Median length785.5
Mean length323.27578
Min length1

Characters and Unicode

Total characters14365729
Distinct characters429
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44173 ?
Unique (%)99.4%

Sample

1st rowLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.
2nd rowWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.
3rd rowA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.
4th rowCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.
5th rowJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.
ValueCountFrequency (%)
the 138082
 
5.6%
a 98889
 
4.0%
and 75259
 
3.1%
to 73321
 
3.0%
of 69574
 
2.8%
in 48143
 
2.0%
is 36500
 
1.5%
his 36165
 
1.5%
with 23902
 
1.0%
her 21484
 
0.9%
Other values (97091) 1827392
74.6%
2024-10-30T20:40:22.775949image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2406350
16.8%
e 1363796
 
9.5%
a 940505
 
6.5%
t 934766
 
6.5%
i 851514
 
5.9%
o 829873
 
5.8%
n 822601
 
5.7%
s 767854
 
5.3%
r 744274
 
5.2%
h 600810
 
4.2%
Other values (419) 4103386
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14365729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2406350
16.8%
e 1363796
 
9.5%
a 940505
 
6.5%
t 934766
 
6.5%
i 851514
 
5.9%
o 829873
 
5.8%
n 822601
 
5.7%
s 767854
 
5.3%
r 744274
 
5.2%
h 600810
 
4.2%
Other values (419) 4103386
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14365729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2406350
16.8%
e 1363796
 
9.5%
a 940505
 
6.5%
t 934766
 
6.5%
i 851514
 
5.9%
o 829873
 
5.8%
n 822601
 
5.7%
s 767854
 
5.3%
r 744274
 
5.2%
h 600810
 
4.2%
Other values (419) 4103386
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14365729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2406350
16.8%
e 1363796
 
9.5%
a 940505
 
6.5%
t 934766
 
6.5%
i 851514
 
5.9%
o 829873
 
5.8%
n 822601
 
5.7%
s 767854
 
5.3%
r 744274
 
5.2%
h 600810
 
4.2%
Other values (419) 4103386
28.6%

popularity
Unsupported

Rejected  Unsupported 

Missing2
Missing (%)< 0.1%
Memory size1.7 MiB

production_companies
Text

Missing 

Distinct22666
Distinct (%)67.5%
Missing11799
Missing (%)26.0%
Memory size3.5 MiB
2024-10-30T20:40:23.527949image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length609
Median length412
Mean length41.494253
Min length2

Characters and Unicode

Total characters1393377
Distinct characters294
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20300 ?
Unique (%)60.5%

Sample

1st rowPixar Animation Studios
2nd rowTriStar Pictures, Teitler Film, Interscope Communications
3rd rowWarner Bros., Lancaster Gate
4th rowTwentieth Century Fox Film Corporation
5th rowSandollar Productions, Touchstone Pictures
ValueCountFrequency (%)
films 9455
 
5.3%
pictures 9267
 
5.2%
productions 9059
 
5.1%
film 6679
 
3.8%
entertainment 5154
 
2.9%
corporation 2189
 
1.2%
company 1769
 
1.0%
warner 1478
 
0.8%
bros 1411
 
0.8%
the 1381
 
0.8%
Other values (18616) 129839
73.1%
2024-10-30T20:40:24.663908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
144110
 
10.3%
i 106938
 
7.7%
e 94644
 
6.8%
n 89969
 
6.5%
o 85292
 
6.1%
r 83547
 
6.0%
t 83433
 
6.0%
a 77143
 
5.5%
s 62667
 
4.5%
l 51264
 
3.7%
Other values (284) 514370
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1393377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
144110
 
10.3%
i 106938
 
7.7%
e 94644
 
6.8%
n 89969
 
6.5%
o 85292
 
6.1%
r 83547
 
6.0%
t 83433
 
6.0%
a 77143
 
5.5%
s 62667
 
4.5%
l 51264
 
3.7%
Other values (284) 514370
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1393377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
144110
 
10.3%
i 106938
 
7.7%
e 94644
 
6.8%
n 89969
 
6.5%
o 85292
 
6.1%
r 83547
 
6.0%
t 83433
 
6.0%
a 77143
 
5.5%
s 62667
 
4.5%
l 51264
 
3.7%
Other values (284) 514370
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1393377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
144110
 
10.3%
i 106938
 
7.7%
e 94644
 
6.8%
n 89969
 
6.5%
o 85292
 
6.1%
r 83547
 
6.0%
t 83433
 
6.0%
a 77143
 
5.5%
s 62667
 
4.5%
l 51264
 
3.7%
Other values (284) 514370
36.9%
Distinct2392
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-10-30T20:40:25.391912image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length1039
Median length649
Mean length53.279821
Min length2

Characters and Unicode

Total characters2417785
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1767 ?
Unique (%)3.9%

Sample

1st row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
2nd row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
3rd row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
4th row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
5th row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
ValueCountFrequency (%)
iso_3166_1 49408
18.1%
name 49408
18.1%
united 25266
9.2%
states 21148
7.7%
of 21147
7.7%
america 21147
7.7%
us 21147
7.7%
6211
 
2.3%
gb 4091
 
1.5%
kingdom 4091
 
1.5%
Other values (344) 50128
18.3%
2024-10-30T20:40:26.471902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 395259
16.3%
227813
 
9.4%
e 130057
 
5.4%
a 119896
 
5.0%
i 107956
 
4.5%
6 98817
 
4.1%
_ 98816
 
4.1%
1 98816
 
4.1%
: 98816
 
4.1%
n 96903
 
4.0%
Other values (59) 944636
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2417785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 395259
16.3%
227813
 
9.4%
e 130057
 
5.4%
a 119896
 
5.0%
i 107956
 
4.5%
6 98817
 
4.1%
_ 98816
 
4.1%
1 98816
 
4.1%
: 98816
 
4.1%
n 96903
 
4.0%
Other values (59) 944636
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2417785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 395259
16.3%
227813
 
9.4%
e 130057
 
5.4%
a 119896
 
5.0%
i 107956
 
4.5%
6 98817
 
4.1%
_ 98816
 
4.1%
1 98816
 
4.1%
: 98816
 
4.1%
n 96903
 
4.0%
Other values (59) 944636
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2417785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 395259
16.3%
227813
 
9.4%
e 130057
 
5.4%
a 119896
 
5.0%
i 107956
 
4.5%
6 98817
 
4.1%
_ 98816
 
4.1%
1 98816
 
4.1%
: 98816
 
4.1%
n 96903
 
4.0%
Other values (59) 944636
39.1%
Distinct17333
Distinct (%)38.2%
Missing3
Missing (%)< 0.1%
Memory size354.7 KiB
Minimum1874-12-09 00:00:00
Maximum2020-12-16 00:00:00
2024-10-30T20:40:26.775901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:27.095902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

revenue
Real number (ℝ)

High correlation  Zeros 

Distinct6863
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11229357
Minimum0
Maximum2.7879651 × 109
Zeros37972
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size354.7 KiB
2024-10-30T20:40:27.391912image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile48018459
Maximum2.7879651 × 109
Range2.7879651 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64387893
Coefficient of variation (CV)5.7338897
Kurtosis237.09288
Mean11229357
Median Absolute Deviation (MAD)0
Skewness12.255124
Sum5.0957698 × 1011
Variance4.1458008 × 1015
MonotonicityNot monotonic
2024-10-30T20:40:27.679900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37972
83.7%
12000000 20
 
< 0.1%
10000000 19
 
< 0.1%
11000000 19
 
< 0.1%
2000000 18
 
< 0.1%
6000000 17
 
< 0.1%
5000000 14
 
< 0.1%
8000000 13
 
< 0.1%
500000 13
 
< 0.1%
1 12
 
< 0.1%
Other values (6853) 7262
 
16.0%
ValueCountFrequency (%)
0 37972
83.7%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2787965087 1
< 0.1%
2068223624 1
< 0.1%
1845034188 1
< 0.1%
1519557910 1
< 0.1%
1513528810 1
< 0.1%
1506249360 1
< 0.1%
1405403694 1
< 0.1%
1342000000 1
< 0.1%
1274219009 1
< 0.1%
1262886337 1
< 0.1%

runtime
Real number (ℝ)

Zeros 

Distinct353
Distinct (%)0.8%
Missing249
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean94.181675
Minimum0
Maximum1256
Zeros1535
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size354.7 KiB
2024-10-30T20:40:27.991947image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q185
median95
Q3107
95-th percentile138
Maximum1256
Range1256
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.341059
Coefficient of variation (CV)0.4070968
Kurtosis93.925543
Mean94.181675
Median Absolute Deviation (MAD)11
Skewness4.4907363
Sum4250419
Variance1470.0368
MonotonicityNot monotonic
2024-10-30T20:40:28.303902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2549
 
5.6%
0 1535
 
3.4%
100 1470
 
3.2%
95 1410
 
3.1%
93 1214
 
2.7%
96 1104
 
2.4%
92 1079
 
2.4%
94 1062
 
2.3%
91 1055
 
2.3%
88 1030
 
2.3%
Other values (343) 31622
69.7%
ValueCountFrequency (%)
0 1535
3.4%
1 107
 
0.2%
2 33
 
0.1%
3 48
 
0.1%
4 50
 
0.1%
5 51
 
0.1%
6 72
 
0.2%
7 103
 
0.2%
8 78
 
0.2%
9 63
 
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%

spoken_languages
Text

Missing 

Distinct1841
Distinct (%)4.4%
Missing3894
Missing (%)8.6%
Memory size3.0 MiB
2024-10-30T20:40:28.735942image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length171
Median length7
Mean length9.3978546
Min length2

Characters and Unicode

Total characters389870
Distinct characters171
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1293 ?
Unique (%)3.1%

Sample

1st rowEnglish
2nd rowEnglish, Français
3rd rowEnglish
4th rowEnglish
5th rowEnglish
ValueCountFrequency (%)
english 28729
52.8%
français 4194
 
7.7%
deutsch 2624
 
4.8%
español 2412
 
4.4%
italiano 2366
 
4.4%
日本語 1758
 
3.2%
pусский 1562
 
2.9%
普通话 790
 
1.5%
हिन्दी 707
 
1.3%
663
 
1.2%
Other values (69) 8565
 
15.8%
2024-10-30T20:40:29.511900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 42270
10.8%
n 37462
 
9.6%
i 37109
 
9.5%
l 34631
 
8.9%
h 31459
 
8.1%
E 31198
 
8.0%
g 30413
 
7.8%
a 18946
 
4.9%
13079
 
3.4%
, 11666
 
3.0%
Other values (161) 101637
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 389870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 42270
10.8%
n 37462
 
9.6%
i 37109
 
9.5%
l 34631
 
8.9%
h 31459
 
8.1%
E 31198
 
8.0%
g 30413
 
7.8%
a 18946
 
4.9%
13079
 
3.4%
, 11666
 
3.0%
Other values (161) 101637
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 389870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 42270
10.8%
n 37462
 
9.6%
i 37109
 
9.5%
l 34631
 
8.9%
h 31459
 
8.1%
E 31198
 
8.0%
g 30413
 
7.8%
a 18946
 
4.9%
13079
 
3.4%
, 11666
 
3.0%
Other values (161) 101637
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 389870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 42270
10.8%
n 37462
 
9.6%
i 37109
 
9.5%
l 34631
 
8.9%
h 31459
 
8.1%
E 31198
 
8.0%
g 30413
 
7.8%
a 18946
 
4.9%
13079
 
3.4%
, 11666
 
3.0%
Other values (161) 101637
26.1%

status
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing83
Missing (%)0.2%
Memory size2.5 MiB
Released
44936 
Rumored
 
230
Post Production
 
97
In Production
 
19
Planned
 
13

Length

Max length15
Median length8
Mean length8.0117229
Min length7

Characters and Unicode

Total characters362899
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 44936
99.0%
Rumored 230
 
0.5%
Post Production 97
 
0.2%
In Production 19
 
< 0.1%
Planned 13
 
< 0.1%
Canceled 1
 
< 0.1%
(Missing) 83
 
0.2%

Length

2024-10-30T20:40:29.799901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-30T20:40:30.119946image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
released 44936
99.0%
rumored 230
 
0.5%
production 116
 
0.3%
post 97
 
0.2%
in 19
 
< 0.1%
planned 13
 
< 0.1%
canceled 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 135053
37.2%
d 45296
 
12.5%
R 45166
 
12.4%
s 45033
 
12.4%
l 44950
 
12.4%
a 44950
 
12.4%
o 559
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
m 230
 
0.1%
Other values (8) 970
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 362899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 135053
37.2%
d 45296
 
12.5%
R 45166
 
12.4%
s 45033
 
12.4%
l 44950
 
12.4%
a 44950
 
12.4%
o 559
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
m 230
 
0.1%
Other values (8) 970
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 362899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 135053
37.2%
d 45296
 
12.5%
R 45166
 
12.4%
s 45033
 
12.4%
l 44950
 
12.4%
a 44950
 
12.4%
o 559
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
m 230
 
0.1%
Other values (8) 970
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 362899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 135053
37.2%
d 45296
 
12.5%
R 45166
 
12.4%
s 45033
 
12.4%
l 44950
 
12.4%
a 44950
 
12.4%
o 559
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
m 230
 
0.1%
Other values (8) 970
 
0.3%

tagline
Text

Missing 

Distinct20269
Distinct (%)99.4%
Missing24981
Missing (%)55.0%
Memory size2.7 MiB
2024-10-30T20:40:30.823901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length297
Median length204
Mean length46.999314
Min length1

Characters and Unicode

Total characters958692
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20163 ?
Unique (%)98.8%

Sample

1st rowRoll the dice and unleash the excitement!
2nd rowStill Yelling. Still Fighting. Still Ready for Love.
3rd rowFriends are the people who let you be yourself... and never let you forget it.
4th rowJust When His World Is Back To Normal... He's In For The Surprise Of His Life!
5th rowA Los Angeles Crime Saga
ValueCountFrequency (%)
the 10998
 
6.3%
a 6815
 
3.9%
of 4404
 
2.5%
to 3584
 
2.1%
is 2796
 
1.6%
in 2693
 
1.5%
and 2682
 
1.5%
you 2389
 
1.4%
1582
 
0.9%
for 1523
 
0.9%
Other values (15100) 134470
77.3%
2024-10-30T20:40:31.887910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153686
16.0%
e 94412
 
9.8%
t 57267
 
6.0%
o 56566
 
5.9%
a 51473
 
5.4%
n 47498
 
5.0%
i 46036
 
4.8%
r 44992
 
4.7%
s 42360
 
4.4%
h 37172
 
3.9%
Other values (160) 327230
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 958692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
153686
16.0%
e 94412
 
9.8%
t 57267
 
6.0%
o 56566
 
5.9%
a 51473
 
5.4%
n 47498
 
5.0%
i 46036
 
4.8%
r 44992
 
4.7%
s 42360
 
4.4%
h 37172
 
3.9%
Other values (160) 327230
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 958692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
153686
16.0%
e 94412
 
9.8%
t 57267
 
6.0%
o 56566
 
5.9%
a 51473
 
5.4%
n 47498
 
5.0%
i 46036
 
4.8%
r 44992
 
4.7%
s 42360
 
4.4%
h 37172
 
3.9%
Other values (160) 327230
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 958692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
153686
16.0%
e 94412
 
9.8%
t 57267
 
6.0%
o 56566
 
5.9%
a 51473
 
5.4%
n 47498
 
5.0%
i 46036
 
4.8%
r 44992
 
4.7%
s 42360
 
4.4%
h 37172
 
3.9%
Other values (160) 327230
34.1%

title
Text

Distinct42196
Distinct (%)93.0%
Missing3
Missing (%)< 0.1%
Memory size2.9 MiB
2024-10-30T20:40:32.599911image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length105
Median length79
Mean length16.701781
Min length1

Characters and Unicode

Total characters757860
Distinct characters287
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39869 ?
Unique (%)87.9%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 14555
 
10.7%
of 4930
 
3.6%
a 2241
 
1.6%
in 1693
 
1.2%
and 1631
 
1.2%
to 1054
 
0.8%
757
 
0.6%
man 665
 
0.5%
love 664
 
0.5%
for 601
 
0.4%
Other values (24353) 107390
78.9%
2024-10-30T20:40:33.655958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90827
 
12.0%
e 76251
 
10.1%
a 48940
 
6.5%
o 45671
 
6.0%
n 40817
 
5.4%
r 40018
 
5.3%
i 39764
 
5.2%
t 36722
 
4.8%
s 29519
 
3.9%
h 28516
 
3.8%
Other values (277) 280815
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 757860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
90827
 
12.0%
e 76251
 
10.1%
a 48940
 
6.5%
o 45671
 
6.0%
n 40817
 
5.4%
r 40018
 
5.3%
i 39764
 
5.2%
t 36722
 
4.8%
s 29519
 
3.9%
h 28516
 
3.8%
Other values (277) 280815
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 757860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
90827
 
12.0%
e 76251
 
10.1%
a 48940
 
6.5%
o 45671
 
6.0%
n 40817
 
5.4%
r 40018
 
5.3%
i 39764
 
5.2%
t 36722
 
4.8%
s 29519
 
3.9%
h 28516
 
3.8%
Other values (277) 280815
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 757860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
90827
 
12.0%
e 76251
 
10.1%
a 48940
 
6.5%
o 45671
 
6.0%
n 40817
 
5.4%
r 40018
 
5.3%
i 39764
 
5.2%
t 36722
 
4.8%
s 29519
 
3.9%
h 28516
 
3.8%
Other values (277) 280815
37.1%

vote_average
Real number (ℝ)

Zeros 

Distinct92
Distinct (%)0.2%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.62407
Minimum0
Maximum10
Zeros2947
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size354.7 KiB
2024-10-30T20:40:33.959944image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.9154225
Coefficient of variation (CV)0.34057587
Kurtosis2.5420547
Mean5.62407
Median Absolute Deviation (MAD)0.9
Skewness-1.524472
Sum255197.8
Variance3.6688434
MonotonicityNot monotonic
2024-10-30T20:40:34.247903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2947
 
6.5%
6 2462
 
5.4%
5 1998
 
4.4%
7 1883
 
4.1%
6.5 1722
 
3.8%
6.3 1603
 
3.5%
5.5 1381
 
3.0%
5.8 1369
 
3.0%
6.4 1350
 
3.0%
6.7 1342
 
3.0%
Other values (82) 27319
60.2%
ValueCountFrequency (%)
0 2947
6.5%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 103
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 185
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 2
 
< 0.1%
9 158
0.3%
8.9 7
 
< 0.1%

vote_count
Real number (ℝ)

High correlation  Zeros 

Distinct1820
Distinct (%)4.0%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean110.09644
Minimum0
Maximum14075
Zeros2849
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size354.7 KiB
2024-10-30T20:40:34.535908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q334
95-th percentile434
Maximum14075
Range14075
Interquartile range (IQR)31

Descriptive statistics

Standard deviation491.74289
Coefficient of variation (CV)4.4664741
Kurtosis150.92858
Mean110.09644
Median Absolute Deviation (MAD)8
Skewness10.440782
Sum4995736
Variance241811.07
MonotonicityNot monotonic
2024-10-30T20:40:34.831910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3242
 
7.1%
2 3127
 
6.9%
0 2849
 
6.3%
3 2785
 
6.1%
4 2478
 
5.5%
5 2097
 
4.6%
6 1747
 
3.8%
7 1570
 
3.5%
8 1359
 
3.0%
9 1194
 
2.6%
Other values (1810) 22928
50.5%
ValueCountFrequency (%)
0 2849
6.3%
1 3242
7.1%
2 3127
6.9%
3 2785
6.1%
4 2478
5.5%
5 2097
4.6%
6 1747
3.8%
7 1570
3.5%
8 1359
3.0%
9 1194
 
2.6%
ValueCountFrequency (%)
14075 1
< 0.1%
12269 1
< 0.1%
12114 1
< 0.1%
12000 1
< 0.1%
11444 1
< 0.1%
11187 1
< 0.1%
10297 1
< 0.1%
10014 1
< 0.1%
9678 1
< 0.1%
9634 1
< 0.1%

release_year
Real number (ℝ)

Distinct135
Distinct (%)0.3%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1991.8812
Minimum1874
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.7 KiB
2024-10-30T20:40:35.135940image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1874
5-th percentile1941
Q11978
median2001
Q32010
95-th percentile2015
Maximum2020
Range146
Interquartile range (IQR)32

Descriptive statistics

Standard deviation24.05536
Coefficient of variation (CV)0.012076704
Kurtosis0.84010576
Mean1991.8812
Median Absolute Deviation (MAD)12
Skewness-1.2248636
Sum90383601
Variance578.66033
MonotonicityNot monotonic
2024-10-30T20:40:35.431918image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 1974
 
4.4%
2015 1905
 
4.2%
2013 1889
 
4.2%
2012 1722
 
3.8%
2011 1667
 
3.7%
2016 1604
 
3.5%
2009 1586
 
3.5%
2010 1501
 
3.3%
2008 1473
 
3.2%
2007 1320
 
2.9%
Other values (125) 28735
63.3%
ValueCountFrequency (%)
1874 1
 
< 0.1%
1878 1
 
< 0.1%
1883 1
 
< 0.1%
1887 1
 
< 0.1%
1888 2
 
< 0.1%
1890 5
 
< 0.1%
1891 6
< 0.1%
1892 3
 
< 0.1%
1893 1
 
< 0.1%
1894 13
< 0.1%
ValueCountFrequency (%)
2020 1
 
< 0.1%
2018 5
 
< 0.1%
2017 532
 
1.2%
2016 1604
3.5%
2015 1905
4.2%
2014 1974
4.4%
2013 1889
4.2%
2012 1722
3.8%
2011 1667
3.7%
2010 1501
3.3%

return
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct5232
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.99915
Minimum0
Maximum12396383
Zeros39998
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size354.7 KiB
2024-10-30T20:40:36.159909image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.5353413
Maximum12396383
Range12396383
Interquartile range (IQR)0

Descriptive statistics

Standard deviation74690.825
Coefficient of variation (CV)113.16806
Kurtosis20674.324
Mean659.99915
Median Absolute Deviation (MAD)0
Skewness138.3341
Sum29950101
Variance5.5787194 × 109
MonotonicityNot monotonic
2024-10-30T20:40:36.487901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39998
88.1%
1 20
 
< 0.1%
2 12
 
< 0.1%
4 11
 
< 0.1%
5 8
 
< 0.1%
3 7
 
< 0.1%
2.5 7
 
< 0.1%
1.333333333 7
 
< 0.1%
1.5 6
 
< 0.1%
7 4
 
< 0.1%
Other values (5222) 5299
 
11.7%
ValueCountFrequency (%)
0 39998
88.1%
5.217391304 × 10-71
 
< 0.1%
7.5 × 10-71
 
< 0.1%
9.375 × 10-71
 
< 0.1%
1.499133126 × 10-61
 
< 0.1%
1.8 × 10-61
 
< 0.1%
1.916666667 × 10-61
 
< 0.1%
3.5 × 10-61
 
< 0.1%
4 × 10-61
 
< 0.1%
5.111111111 × 10-61
 
< 0.1%
ValueCountFrequency (%)
12396383 1
< 0.1%
8500000 1
< 0.1%
4197476.625 1
< 0.1%
2755584 1
< 0.1%
1018619.283 1
< 0.1%
1000000 1
< 0.1%
26881.72043 1
< 0.1%
12890.38667 1
< 0.1%
5330.33945 1
< 0.1%
4133.333333 1
< 0.1%

Interactions

2024-10-30T20:40:10.887949image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:00.287910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:01.951903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:03.735903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:05.319903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:07.183901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:09.231913image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:11.127909image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:00.543909image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:02.183906image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:03.975901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:05.543911image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:07.423910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:09.471910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:11.351915image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:00.767903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:02.391915image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:04.175911image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:05.751951image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:07.711946image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:09.687913image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:11.575902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:00.999906image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:02.615902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:04.407903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:05.983905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:07.959909image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:09.919906image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:11.807902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:01.223911image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:02.823901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:04.623903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:06.191905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:08.191901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:10.143948image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:12.039915image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:01.463917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:03.103905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:04.855911image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:06.415903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:08.439900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:10.375904image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:12.279903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:01.711902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:03.503901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:05.087902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:06.711905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:08.999904image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-30T20:40:10.615907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-10-30T20:40:36.687909image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
budgetrelease_yearreturnrevenueruntimestatusvote_averagevote_count
budget1.0000.1410.7750.6440.2270.0000.0720.484
release_year0.1411.0000.0870.1040.0340.028-0.0090.197
return0.7750.0871.0000.8530.2340.0000.1200.474
revenue0.6440.1040.8531.0000.2540.0000.1270.513
runtime0.2270.0340.2340.2541.0000.0000.1930.290
status0.0000.0280.0000.0000.0001.0000.0190.000
vote_average0.072-0.0090.1200.1270.1930.0191.0000.318
vote_count0.4840.1970.4740.5130.2900.0000.3181.000

Missing values

2024-10-30T20:40:12.655902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-30T20:40:13.367910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-30T20:40:14.295900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

belongs_to_collectionbudgetgenresidoriginal_languageoverviewpopularityproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_countrelease_yearreturn
0Toy Story Collection30000000.0Animation, Comedy, Family862enLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.21.946943Pixar Animation Studios[{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-10-30373554033.081.0EnglishReleasedNaNToy Story7.75415.01995.012.451801
1NaN65000000.0Adventure, Fantasy, Family8844enWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.17.015539TriStar Pictures, Teitler Film, Interscope Communications[{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-15262797249.0104.0English, FrançaisReleasedRoll the dice and unleash the excitement!Jumanji6.92413.01995.04.043035
2Grumpy Old Men Collection0.0Romance, Comedy15602enA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.11.7129Warner Bros., Lancaster Gate[{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-220.0101.0EnglishReleasedStill Yelling. Still Fighting. Still Ready for Love.Grumpier Old Men6.592.01995.00.000000
3NaN16000000.0Comedy, Drama, Romance31357enCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.3.859495Twentieth Century Fox Film Corporation[{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-2281452156.0127.0EnglishReleasedFriends are the people who let you be yourself... and never let you forget it.Waiting to Exhale6.134.01995.05.090760
4Father of the Bride Collection0.0Comedy11862enJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.8.387519Sandollar Productions, Touchstone Pictures[{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-02-1076578911.0106.0EnglishReleasedJust When His World Is Back To Normal... He's In For The Surprise Of His Life!Father of the Bride Part II5.7173.01995.00.000000
5NaN60000000.0Action, Crime, Drama, Thriller949enObsessive master thief, Neil McCauley leads a top-notch crew on various insane heists throughout Los Angeles while a mentally unstable detective, Vincent Hanna pursues him without rest. Each man recognizes and respects the ability and the dedication of the other even though they are aware their cat-and-mouse game may end in violence.17.924927Regency Enterprises, Forward Pass, Warner Bros.[{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-15187436818.0170.0English, EspañolReleasedA Los Angeles Crime SagaHeat7.71886.01995.03.123947
6NaN58000000.0Comedy, Romance11860enAn ugly duckling having undergone a remarkable change, still harbors feelings for her crush: a carefree playboy, but not before his business-focused brother has something to say about it.6.677277Paramount Pictures, Scott Rudin Productions, Mirage Enterprises, Sandollar Productions, Constellation Entertainment, Worldwide, Mont Blanc Entertainment GmbH[{'iso_3166_1': 'DE', 'name': 'Germany'}, {'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-150.0127.0Français, EnglishReleasedYou are cordially invited to the most surprising merger of the year.Sabrina6.2141.01995.00.000000
7NaN0.0Action, Adventure, Drama, Family45325enA mischievous young boy, Tom Sawyer, witnesses a murder by the deadly Injun Joe. Tom becomes friends with Huckleberry Finn, a boy with no future and no family. Tom has to choose between honoring a friendship or honoring an oath because the town alcoholic is accused of the murder. Tom and Huck go through several adventures trying to retrieve evidence.2.561161Walt Disney Pictures[{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-220.097.0English, DeutschReleasedThe Original Bad Boys.Tom and Huck5.445.01995.00.000000
8NaN35000000.0Action, Adventure, Thriller9091enInternational action superstar Jean Claude Van Damme teams with Powers Boothe in a Tension-packed, suspense thriller, set against the back-drop of a Stanley Cup game.Van Damme portrays a father whose daughter is suddenly taken during a championship hockey game. With the captors demanding a billion dollars by game's end, Van Damme frantically sets a plan in motion to rescue his daughter and abort an impending explosion before the final buzzer...5.23158Universal Pictures, Imperial Entertainment, Signature Entertainment[{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-2264350171.0106.0EnglishReleasedTerror goes into overtime.Sudden Death5.5174.01995.01.838576
9James Bond Collection58000000.0Adventure, Action, Thriller710enJames Bond must unmask the mysterious head of the Janus Syndicate and prevent the leader from utilizing the GoldenEye weapons system to inflict devastating revenge on Britain.14.686036United Artists, Eon Productions[{'iso_3166_1': 'GB', 'name': 'United Kingdom'}, {'iso_3166_1': 'US', 'name': 'United States of America'}]1995-11-16352194034.0130.0English, Pусский, EspañolReleasedNo limits. No fears. No substitutes.GoldenEye6.61194.01995.06.072311
belongs_to_collectionbudgetgenresidoriginal_languageoverviewpopularityproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_countrelease_yearreturn
45369NaN0.0NaN67179itSentenced to life imprisonment for illegal activities, Italian International member Giulio Manieri holds on to his political ideals while struggling against madness in the loneliness of his prison cell.0.225051NaN[]1972-01-010.090.0ItalianoReleasedNaNSt. Michael Had a Rooster6.03.01972.00.0
45370NaN0.0Horror, Mystery, Thriller84419enAn unsuccessful sculptor saves a madman named "The Creeper" from drowning. Seeing an opportunity for revenge, he tricks the psycho into murdering his critics.0.222814Universal Pictures[{'iso_3166_1': 'US', 'name': 'United States of America'}]1946-03-290.065.0EnglishReleasedMeet...The CREEPER!House of Horrors6.38.01946.00.0
45371NaN0.0Mystery, Horror390959enIn this true-crime documentary, we delve into the murder spree that was the inspiration for Joe Berlinger's "Book of Shadows: Blair Witch 2".0.076061NaN[]2000-10-220.045.0EnglishReleasedNaNShadow of the Blair Witch7.02.02000.00.0
45372NaN0.0Horror289923enA film archivist revisits the story of Rustin Parr, a hermit thought to have murdered seven children while under the possession of the Blair Witch.0.38645Neptune Salad Entertainment, Pirie Productions[{'iso_3166_1': 'US', 'name': 'United States of America'}]2000-10-030.030.0EnglishReleasedDo you know what happened 50 years before "The Blair Witch Project"?The Burkittsville 77.01.02000.00.0
45373NaN0.0Science Fiction222848enIt's the year 3000 AD. The world's most dangerous women are banished to a remote asteroid 45 million light years from earth. Kira Murphy doesn't belong; wrongfully accused of a crime she did not commit, she's thrown in this interplanetary prison and left to her own defenses. But Kira's a fighter, and soon she finds herself in the middle of a female gang war; where everyone wants a piece of the action... and a piece of her! "Caged Heat 3000" takes the Women-in-Prison genre to a whole new level... and a whole new galaxy!0.661558Concorde-New Horizons[{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-01-010.085.0EnglishReleasedNaNCaged Heat 30003.51.01995.00.0
45374NaN0.0Drama, Action, Romance30840enYet another version of the classic epic, with enough variation to make it interesting. The story is the same, but some of the characters are quite different from the usual, in particular Uma Thurman's very special maid Marian. The photography is also great, giving the story a somewhat darker tone.5.683753Westdeutscher Rundfunk (WDR), Working Title Films, 20th Century Fox Television, CanWest Global Communications[{'iso_3166_1': 'CA', 'name': 'Canada'}, {'iso_3166_1': 'DE', 'name': 'Germany'}, {'iso_3166_1': 'GB', 'name': 'United Kingdom'}, {'iso_3166_1': 'US', 'name': 'United States of America'}]1991-05-130.0104.0EnglishReleasedNaNRobin Hood5.726.01991.00.0
45375NaN0.0Drama111109tlAn artist struggles to finish his work while a storyline about a cult plays in his head.0.178241Sine Olivia[{'iso_3166_1': 'PH', 'name': 'Philippines'}]2011-11-170.0360.0NaNReleasedNaNCentury of Birthing9.03.02011.00.0
45376NaN0.0Action, Drama, Thriller67758enWhen one of her hits goes wrong, a professional assassin ends up with a suitcase full of a million dollars belonging to a mob boss ...0.903007American World Pictures[{'iso_3166_1': 'US', 'name': 'United States of America'}]2003-08-010.090.0EnglishReleasedA deadly game of wits.Betrayal3.86.02003.00.0
45377NaN0.0NaN227506enIn a small town live two brothers, one a minister and the other one a hunchback painter of the chapel who lives with his wife. One dreadful and stormy night, a stranger knocks at the door asking for shelter. The stranger talks about all the good things of the earthly life the minister is missing because of his puritanical faith. The minister comes to accept the stranger's viewpoint but it is others who will pay the consequences because the minister will discover the human pleasures thanks to, ehem, his sister- in -law… The tormented minister and his cuckolded brother will die in a strange accident in the chapel and later an infant will be born from the minister's adulterous relationship.0.003503Yermoliev[{'iso_3166_1': 'RU', 'name': 'Russia'}]1917-10-210.087.0NaNReleasedNaNSatan Triumphant0.00.01917.00.0
45378NaN0.0NaN461257en50 years after decriminalisation of homosexuality in the UK, director Daisy Asquith mines the jewels of the BFI archive to take us into the relationships, desires, fears and expressions of gay men and women in the 20th century.0.163015NaN[{'iso_3166_1': 'GB', 'name': 'United Kingdom'}]2017-06-090.075.0EnglishReleasedNaNQueerama0.00.02017.00.0

Duplicate rows

Most frequently occurring

belongs_to_collectionbudgetgenresidoriginal_languageoverviewproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_countrelease_yearreturn# duplicates
18NaN0.0Thriller, Mystery141971fiRecovering from a nail gun shot to the head and 13 months of coma, doctor Pekka Valinta starts to unravel the mystery of his past, still suffering from total amnesia.Filmiteollisuus Fine[{'iso_3166_1': 'FI', 'name': 'Finland'}]2008-12-260.0108.0suomiReleasedWhich one is the first to return - memory or the murderer?Blackout6.73.02008.00.03
0Pokémon Collection0.0Adventure, Fantasy, Animation, Science Fiction, Family12600jaAll your favorite Pokémon characters are back, and are joined for the first time by the legendary Pokémon Celebi and Suicune, in this latest exciting Pokémon adventure! In order to escape a greedy Pokémon hunter, Celebi must use the last of its energy to travel through time to the present day. Celebi brings along Sammy, a boy who had been trying to protect it. Along with Ash, Pikachu, and the rest of the gang, Sammy and Celebi encounter an enemy far more advanced than the hunter left behind in the past. This new enemy possesses a Pokéball called a “Dark Ball,” which transforms the Pokémon it captures into evil and far stronger creatures. When Celebi is captured, the fate of the entire forest is threatened. Let POKÉMON 4EVER transport you to a world of adventure as Ash, Suicune and the rest take action to save the day!NaN[{'iso_3166_1': 'JP', 'name': 'Japan'}, {'iso_3166_1': 'US', 'name': 'United States of America'}]2001-07-0628023563.075.0日本語ReleasedNaNPokémon 4Ever: Celebi - Voice of the Forest5.782.02001.00.02
1Why We Fight0.0Documentary159849enThe third film of Frank Capra's 'Why We Fight" propaganda film series, dealing with the Nazi conquest of Western Europe in 1940.NaN[{'iso_3166_1': 'US', 'name': 'United States of America'}]1943-01-010.057.0EnglishReleasedNaNWhy We Fight: Divide and Conquer5.01.01943.00.02
2NaN0.0Action, Drama, Romance, Adventure99080enOriginally called White Thunder, American producer Varick Frissell's 1931 film was inspired by his love for the Canadian Arctic Circle. Set in a beautifully black-and-white filmed Newfoundland, it is the story of a rivalry between two seal hunters that plays out on the ice floes during a hunt. Unsatisfied with the first cut, Frissell arranged for the crew to accompany an actual Newfoundland seal hunt on The SS Viking, on which an explosion of dynamite (carried regularly at the time on Arctic ships to combat ice jams) killed many members of the crew, including Frissell. The film was renamed in honor of the dead.NaN[]1931-06-210.070.0EnglishReleasedActually produced during the Great Newfoundland Seal Hunt and You see the REAL thingThe Viking0.00.01931.00.02
3NaN0.0Action, Horror, Science Fiction18440enWhen a comet strikes Earth and kicks up a cloud of toxic dust, hundreds of humans join the ranks of the living dead. But there's bad news for the survivors: The newly minted zombies are hell-bent on eradicating every last person from the planet. For the few human beings who remain, going head to head with the flesh-eating fiends is their only chance for long-term survival. Yet their battle will be dark and cold, with overwhelming odds.NaN[{'iso_3166_1': 'US', 'name': 'United States of America'}]2007-01-010.089.0EnglishReleasedNaNDays of Darkness5.05.02007.00.02
4NaN0.0Adventure, Animation, Drama, Action, Foreign23305enIn feudal India, a warrior (Khan) who renounces his role as the longtime enforcer to a local lord becomes the prey in a murderous hunt through the Himalayan mountains.Filmfour[{'iso_3166_1': 'FR', 'name': 'France'}, {'iso_3166_1': 'DE', 'name': 'Germany'}, {'iso_3166_1': 'IN', 'name': 'India'}, {'iso_3166_1': 'GB', 'name': 'United Kingdom'}]2001-09-230.086.0हिन्दीReleasedNaNThe Warrior6.315.02001.00.02
5NaN0.0Comedy97995enAfter breaking a mirror in his home, superstitious Max tries to avoid situations which could bring bad luck but in doing so, causes himself the worst luck imaginable.Max Linder Productions[{'iso_3166_1': 'US', 'name': 'United States of America'}]1921-02-060.062.0EnglishReleasedNaNSeven Years Bad Luck5.64.01921.00.02
6NaN0.0Comedy, Drama11115enAs an ex-gambler teaches a hot-shot college kid some things about playing cards, he finds himself pulled into the world series of poker, where his protégé is his toughest competition.Andertainment Group, Crescent City Pictures, Tag Entertainment[{'iso_3166_1': 'US', 'name': 'United States of America'}]2008-01-290.085.0EnglishReleasedNaNDeal5.222.02008.00.02
7NaN0.0Comedy, Drama265189svWhile holidaying in the French Alps, a Swedish family deals with acts of cowardliness as an avalanche breaks out.Motlys, Coproduction Office, Film i Väst[{'iso_3166_1': 'NO', 'name': 'Norway'}, {'iso_3166_1': 'SE', 'name': 'Sweden'}, {'iso_3166_1': 'FR', 'name': 'France'}]2014-08-151359497.0118.0Français, Norsk, svenska, EnglishReleasedNaNForce Majeure6.8255.02014.00.02
8NaN0.0Crime, Drama, Thriller5511frHitman Jef Costello is a perfectionist who always carefully plans his murders and who never gets caught.Fida cinematografica, Compagnie Industrielle et Commerciale Cinématographique (CICC), TC Productions, Filmel[{'iso_3166_1': 'FR', 'name': 'France'}, {'iso_3166_1': 'IT', 'name': 'Italy'}]1967-10-2539481.0105.0FrançaisReleasedThere is no solitude greater than that of the SamuraiLe Samouraï7.9187.01967.00.02